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Ensuring the correctness of road network data is critical for navigation, traffic control and urban planning. Errors like missing roads and absent connections can hinder its quality. Trajectory data emerges as a cost-effective source to uncover such errors. Existing methods often analyze the mismatches between trajectories and road networks to identify specific errors. They heavily rely on manually established rules and fail to fully leverage the diverse patterns of trajectories and the underlying road network structure. The article introduces a sequential classification approach to detect diverse road network errors. It starts with partial map matching (PMM) to associate trajectories with a road network, allowing unmatched portions. Context features are subsequently extracted by encoding patterns in the map matching (MM) outputs, raw trajectories and road network. Finally, a bidirectional recurrent neural network (BiRNN) model is trained to identify the network error category for each trajectory point. Experiments were performed on detecting errors in OpenStreetMap (OSM) road network with a real-world trajectory dataset. It demonstrates that the proposed method achieves accuracy over 96%, significantly surpassing four baselines. An ablation study confirms the necessity of considering different types of context features. This method advances error detection by effectively utilizing trajectories in identifying diverse network errors. [ABSTRACT FROM AUTHOR]

This manuscript presents an Adam optimization-based Deep Reinforcement Learning model for Mixed Traffic Flow control (ADRL-MTF), so as to guide Connected and Autonomous vehicle's (CAV) longitudinal trajectory on a typical urban roadway with signal-controlled intersections. Two improvements are made when compared with prior literatures. First, the common assumptions to simplify the problem solving, such as dividing a vehicle trajectory into several segments with constant acceleration/deceleration, are avoided, to improve the modeling realism. Second, built on the efficient Adam Optimization and Deep Q-Learning, the proposed model avoids the enumeration of states and actions, and is computational efficient and suitable for real time applications. The mixed traffic flow dynamic is firstly formulated as a finite Markov decision process (MDP) model. Due to the discretization of time, space and speed, this MDP model becomes high-dimensional in state, and is very challenging to solve. We then propose a temporal difference-based deep reinforcement learning approach, with ε -greedy for exploration-exploitation balance. Two neural networks are developed to replace the traditional Q function and generate the targets in the Q-learning update. These two neural networks are trained by the Adam optimization algorithm, which extends stochastic gradient descent and considers the second moments of the gradients, and is thus highly computational efficient and has lower memory requirements. The proposed model is shown to reduce fuel consumption by 7.8%, which outperforms a prior benchmark model based on Monte Carlo Tree Search. The model's runtime efficiency and stability are tested, and the sensitivity analysis is also performed. [ABSTRACT FROM AUTHOR]

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

Concerning the critical role of Intelligent Transportation System (ITS) in tackling the problem of traffic congestion, and enhancing the safety, mobility and sustainability of the transport network, this paper provides a comprehensive review of recent developments in Connected Vehicle (CV) technology, its features and terminology. The real-world implementation of the CV technologies can result in a better understanding of the practical and technical issues. Given the technical and conceptual challenges, as well as novelty factors associated with CVs, many CV pilot studies around the world are reviewed, and their findings are summarized. The paper contributes to the literature by reviewing the characteristics, communication platform, multimodality, size and type of study areas of the pilot CV testbeds across the world. This research also paves the way for the better development of CV technology by comparing and discussing the pros and cons of the existing CV pilot testbeds. Moreover, we have discussed the evaluation plans and the advantages and disadvantages of different testbeds. Besides, as a main component of the CV system, the traffic signal control algorithms, as well as transit signal priority in a CV ecosystem, are also reviewed. Based on our findings, the real-time data obtained from the CV system could result in a better adjustment of traffic signal parameters which consequently enhance the traffic regulation in the network (say up to 30% in terms of travel time in comparison with an actuated coordinated traffic signal). However, a major criticism of the existing traffic signal strategies in a CV system is that they do not utilize the maximum capability of the CV system (V2 V, V2I and I2I communications). [ABSTRACT FROM AUTHOR]

The width of street rights-of-way is normally determined by traffic engineering and urban design conventions, without considering the immense value of the underlying land. In this article, I develop an economic framework that can inform decisions on street width, and I use tax parcel data to quantify the widths, land areas, and land value of streets in 20 of the largest counties in the United States. Residential street rights-of-way in the urbanized portion of these counties average 55 ft wide, far greater than the functional minimum of 16 ft required for access. The land value of residential streets totals $959 billion in the urbanized portion of the 20-county sample. In most counties, subdivision regulations are binding. That is, few developers choose to build streets that are wider than code requirements, implying that softening requirements would mean more land devoted to housing and less to streets. Although I highlight the potential for narrower street rights-of-way, I did not consider detailed design issues. Nor did I analyze how any windfall from reduced land requirements would be divided among landowners, developers, and house purchasers. Particularly in places with high land values and housing costs, reallocating street rights-of-way to housing would increase economic efficiency. In the most expensive county in the data set—Santa Clara (CA)—narrowing the right-of-way to 16 ft would save more than $100,000 per housing unit through reduced land consumption. Where streets have little or no function for through traffic, the costs and benefits accrue almost exclusively to neighborhood residents. Thus, planners could reduce or even eliminate street width requirements in subdivision ordinances, leaving developers to make the trade-off between land for streets and land for housing. [ABSTRACT FROM AUTHOR]

A signalized intersection regulated by pre-timed control in an urban traffic network is considered. A method for computing the mean delay of vehicles based on the theory of queues with server vacations is adapted from the existing literature and an algorithm for numerical implementation is proposed. Arrivals are assumed to occur according to a batch Markovian arrival process (BMAP). The proposed algorithm is an alternative to the previous methods that includes a numerical improvement in the computation of the queue invariant probabilities. In the case of isolated intersections we compare the new algorithm with other traffic models, including simulation model, Webster model and the Highway Capacity Manual model. The mean delay of vehicles given by the new algorithm is a short distance away from the mean delay obtained in the simulations. The approximation for large flows given by the proposed algorithm is as good as the Highway Capacity Manual model and with no need to tune unknown parameters. Our results are of interest for traffic engineers, since other approaches have been subject to criticism when flows near capacity are involved. [ABSTRACT FROM AUTHOR]